Currently a database can be physically implemented in order to store an extremely voluminous amount of data. Furthermore, one common technique for organizing data stored by a database is referred to as a relational database, wherein the stored data is organized into different tables. Each table of data of the relational database can be interrelated to one or more additional tables of data. Moreover, it is appreciated that a wide variety of different types of information and data can be stored by a commonly shared relational database. For example, one type of data that can be stored by the relational database is referred to as metadata, which is generally understood to be information about other data. Therefore, metadata can be useful to the relational database user. As such, it can be desirable for the relational database user to search or query the relational database in order to retrieve metadata from it. One conventional approach for users to retrieve metadata associated with a relational database is to utilize a database searching application such as SQL (structured query language). However, there are disadvantages associated with these types of database searching applications.
For example, one of the disadvantages is that the database user (or someone else) has to write complicated “select” statements, which the database engine will utilize to retrieve the metadata of interest. Note that in order to write the complicated “select” statements, the user typically consults the product documentation associated with the relational database to determine which tables contain the pieces of information for assembling the metadata query. As such, this process can be time consuming and expensive. Furthermore, the process is typically repeated to query another relational database for metadata since its relational organization is different from the first relational database.
As such, it is desirable to address one or more of the above issues.